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ml-pipeline-workflow

Build end-to-end MLOps pipelines from data preparation through model training

ml-pipeline-workflow is an agent skill from the wshobson/agents collection for building end-to-end MLOps pipelines that cover the full machine learning lifecycle. The pipeline runs from data ingestion and preparation through training, validation and deployment to monitoring, and each step feeds the next in an automated, repeatable way. The skill gives guidance for designing the workflow orchestration of an ML system, including DAG-based orchestration in which each step is a node that runs after its dependencies finish. It covers implementing automation from data to model to deployment, setting up reproducible training workflows so that a model can be rebuilt from the same inputs, and integrating ML components into a production system. It applies when creating an ML pipeline from scratch, designing workflow orchestration for ML systems, automating data-to-model-to-deployment steps, setting up reproducible training, and connecting models to production services. The skill describes what a good pipeline provides, which helps a team decide what to build first. Pipelines depend on the team's own data stores, compute and deployment targets, so the agent adapts the patterns to those and the team reviews the result. It is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 10.9K installs and passing results from three security audits at the time of the scan.